How Smart Data Processing Can Save Indian Hospitals Millions In Wasted Resources

Startups in India's healthcare AI sector increasingly face a familiar challenge: fragmented data environments that force organizations to spend months cleaning records before any meaningful analysis can begin. The combination of poor data quality, disparate hospital record systems, and requirements under India's Digital Personal Data Protection Act creates significant bottlenecks in processing patient information across healthcare facilities. These delays translate directly into wasted resources and diminished patient care quality.

A recent implementation at the Maryland Department of Health offers relevant insights for this challenge. Vamshi Paili, a Senior Data Processing Engineer at FEI Systems in Maryland, has implemented solutions that yield significant, measurable results through targeted interventions. His work on the Long-Term Services and Supports (LTSS) project is a prime example, where his optimizations have led to 99% uptime for critical data workflows. Furthermore, his award-winning AI knowledge assistant, AIRA, cut information search time by 30% and reduced project delays by 40% within his company.

The similarities between Maryland's healthcare data issues and those of Indian hospitals are striking. Both face fragmented records, resistance to workflow changes, and the challenge of maintaining patient care quality while enhancing backend efficiency.

Processing Delays Cost More Than Money

The inefficiencies with processing data in healthcare result in a chain reaction of problems for the entire medical organisation. Paili's work in the healthcare system in the state of Maryland showed how an inefficiency in processing documentation would ultimately affect all aspects of care. "Inefficiencies lead to bottlenecks in coordination, delays in care, and have employees spending too much time processing paperwork instead of caring for patients," Paili said. His Master's in Applied Machine Intelligence from Northeastern University equipped him with the tools to provide unique solutions to healthcare.

At FEI Systems, an organisation with over 500 employees, and some of its largest clients including the Maryland Department of Health, Paili identified workflow inefficiencies that certainly mirror the problems experienced in hospitals in India. Paili describes how the delays in processing documents were not just an inconvenience but directly affected patient outcomes and left healthcare workers frustrated while waiting for key pieces of information to provide care.

Incremental Solutions Deliver Major Results

Instead of seeking to replace whole systems, Paili focused on targeted interventions. His project, AIRA, an AI-powered knowledge assistant created with LangChain and Streamlit, won first place and a $5,000 prize in FEI's 2024 AI hackathon by solving critical inefficiencies without disrupting established processes.

"Integrating into existing systems was much more effective than replacement," Paili recalls, whose previous hackathon victories at HackBoston 2022 and Hackatron S3 2022 included similar focused solutions. "Healthcare workers were comfortable with their interfaces and workflows – they needed enhanced capabilities, not a whole new way of doing things."

The LTSS Project demonstrates this principle at scale. By focusing on integration, the project achieved concrete results like a 3-hour reduction in release cycle times and 99% uptime across more than 600 daily automated jobs. This approach respects that healthcare workers will resist changes that disrupt healthcare workers, proving that measurable improvements don't require system-wide replacements.

Technology Meets Healthcare Reality

Paili has contributed technically in ways that extend beyond projects. His Revere.city dashboard provides real-time civic data for municipal officials, and his work on synthetic data solutions tackles privacy issues in healthcare analytics. Each project examined a larger context of how technology can complement human work rather than displace it.

"Healthcare workers want to see immediate value in new systems without redefining their workflows completely," Paili says.

A prominent takeaway from Paili's work centres on user adoption: when entering healthcare, technical excellence is less important than user acceptance. In order for healthcare professionals to use a new tool, it must integrate seamlessly with their current workflow while yielding clear advantages.

Beyond Individual Projects

Since March 2023, Paili's senior role at FEI Systems has focused on the critical data workflows for the Maryland Department of Health's LTSS project. His technical leadership ensures the reliable processing of sensitive patient records and claims. His first-place, C-suite-approved hackathon project, AIRA, demonstrated this innovative capacity.

"My work involves designing and optimizing the data pipelines that are the backbone of the LTSS system," says Paili. "The goal is to ensure data moves securely and without interruption, which is essential for providers to get paid and for patients to receive care."

FEI Systems' established reputation in healthcare IT solutions, built through work with major federal agencies like the Centers for Medicare & Medicaid Services, has helped Paili provide innovative input. It is important to remember that if working with established healthcare organisations, most importantly, you also have to keep in mind the organisation's existing capabilities and barriers.

Scaling Success Across Borders

Paili's current work takes his healthcare data expertise in new directions. His work on synthetic data solutions starts to address privacy issues in healthcare analytics.

Paili encourages healthcare organisations to "Choose one daily pain point for staff and build targeted solutions aligned with existing processes instead of aiming for a complete redesign." He should know: he has worked with dozens of projects as a judge in competitions such as Cases and Faces 2025.

The success of this exact approach at the Maryland Department of Health (MDH) proves his point. It wasn't measured in a single metric but in tangible outcomes: near-perfect system uptime, faster and more reliable software deployments, and seamless data exchange between state agencies. Likewise, Paili's award-winning AIRA project received accolades from FEI's C-suite executives because it solved a real, quantifiable business problem: reducing wasted staff time and project delays.

Indian healthcare facilities, similarly experiencing data processing challenges, will benefit from looking at these principles. Instead of putting out requests for proposals to develop an "end-to-end" digital transformation, successful implementations are those that are focused on the specific workflow problems that create the most frustration for a medical staff role. As such, small efficiencies in handling specific data are likely to produce significant operational benefits.

Published on: Tuesday, November 11, 2025, 08:17 PM IST